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EHRA/EAPCI expert consensus statement on catheter-based left atrial appendage occlusion – an update Prognostic Implication of Thermodilution Coronary Flow Reserve in Patients Undergoing Fractional Flow Reserve Measurement Validation of bifurcation DEFINITION criteria and comparison of stenting strategies in true left main bifurcation lesions Pulmonary Artery Denervation Using Catheter based Ultrasonic Energy Prognostic implications of ischemia with nonobstructive coronary arteries (INOCA): Understanding risks for improving treatment Fractional Flow Reserve-Guided Complete Revascularization Improves the Prognosis in Patients With ST-Segment-Elevation Myocardial Infarction and Severe Nonculprit Disease: A DANAMI 3-PRIMULTI Substudy (Primary PCI in Patients With ST-Elevation Myocardial Infarction and Multivessel Disease: Treatment Physiologic Characteristics and Clinical Outcomes of Patients With Discordance Between FFR and iFR Lysed Erythrocyte Membranes Promote Vascular Calcification: Possible Role of Erythrocyte-Derived Nitric Oxide Left Main Bifurcation Angioplasty: Are 2 Stents One Too Many? Increased pulmonary serotonin transporter in patients with chronic obstructive pulmonary disease who developed pulmonary hypertension
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Review Article2020 Jul 16;229:1-17.

JOURNAL：Am Heart J . Article Link

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure

CR Olsen, RJ Mentz, KJ Anstrom et al. Keywords: machine learning; artificial intelligence;

ABSTRACT

Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and management of heart failure. Many physicians are familiar with these terms and the excitement surrounding them, but many are unfamiliar with the basics of these algorithms and how they are applied to medicine. Within heart failure research, current applications of machine learning include creating new approaches to diagnosis, classifying patients into novel phenotypic groups, and improving prediction capabilities. In this paper, we provide an overview of machine learning targeted for the practicing clinician and evaluate current applications of machine learning in the diagnosis, classification, and prediction of heart failure.

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